Act as a concise, evidence-driven expert advisor.
Priorities: Accuracy > Relevance > Clarity > Actionability > Brevity.
Lead with the answer. Use the shortest response that fully answers the question.
Do not invent facts, sources, data, or certainty. State uncertainty explicitly when relevant.
Treat the user as knowledgeable. Match their expertise. Avoid condescension, filler, repetition, motivational language, and unnecessary context.
As of the time of this writing, Cloudflare Workers CI does not work with the newer text-based bun.lock file and instead requires a bun.lockb file to build your app via their CI system.
This usually manifests itself with the following error when trying to deploy your apps using a bun.lock file:
13:48:54.691 Initializing build environment...
13:49:02.821 Success: Finished initializing build environment
13:49:03.776 Cloning repository...
AGENTS.md has emerged as the de facto open standard for guiding AI coding assistants, now adopted by over 20,000 repositories and formalized in August 2025 through collaboration between OpenAI, Google, Cursor, Factory, and Sourcegraph. This file acts as a "README for machines"—providing structured, technical context that helps AI assistants write better code from the start. For Python + AWS + Terraform projects, a well-crafted AGENTS.md dramatically reduces friction, ensuring generated code follows your conventions, uses the right tools, and adheres to security requirements.
AGENTS.md is a dedicated Markdown file that complements, not replaces, README.md. While README targets human developers with project overviews and quick-start guides, AGENTS.md contains detailed technical instructions specifically for AI coding agents. Think of it as onboarding documentation for an AI team member: ex
该 FAQ 并非意图一次性提供所有必要信息, 仅仅是提供必要的指路. 最后的杂项部分是宝藏区, 建议都翻翻看啦!
查看最新消息参阅 SD RESOURCE GOLDMINE 2 (English), 或者 sudoskys/StableDiffusionBook (中文)
建议优先阅读官方文档或者 SD RESOURCE GOLDMINE 或者 VOLDY RETARD GUIDE或者としあきdiffusion
| {#--------------------------------------------------------------------- | |
| custom_pub_chat_template_gemma4.jinja | |
| ===================================== | |
| A public, harness-friendly fork of Google's Gemma 4 chat template, | |
| tuned for open-source agentic coding harnesses like: | |
| - anomalyco/opencode (https://github.com/anomalyco/opencode) | |
| - earendil-works/pi (https://github.com/earendil-works/pi) | |
| - openclaw, OpenHarness, similar Claude-Code-style harnesses | |
| WHY THIS FORK EXISTS |
If you often connect to your Linux server using VSCode, the "~/.vscode-server/" folder (and sometimes the ~/.cache/ folder too) can get very large because VSCode:
If your server storage space is limited, you might consider cleaning "~/.vscode-server/" (and ~/.cache/) regularly using the bash script I wrote.
Simply run the following command:
curl -sL https://gist.githubusercontent.com/XDflight/5f3509eb84fc282b88059c909036f5bc/raw/07aa29746c9b085fce84702967bdfe6ac019c8b6/clean_vscode-server.sh | bash -s
Project 1 (Current - my-project) default settings:
54321, DB: 54322, DB.POOLER: 54329, Studio: 54323, Inbucket: 54324, Analytics: 54327, Edge Inspector: 8382Project 2 (New - my-project-2):
54331, DB: 54332, DB.POOLER: 54339, Studio: 54333, Inbucket: 54334, Analytics: 54337, Edge Inspector: 8383
| # AGENTS.md | |
| ## Engineering Philosophy | |
| This project values small, direct, understandable code. | |
| Prefer the simplest implementation that solves the problem without creating downstream complexity. Simple does not mean simplistic. It means the shortest clear path from point A to point B, with the fewest moving parts required to keep the system correct, maintainable, and easy to reason about. | |
| Avoid enterprise-style ceremony unless it is clearly justified by the problem. |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
"It is not possible to unreserve more products of <product name> than you have in stock"
Sometimes, the reserved_quantity on the stock.quant record for a product becomes out-of-sync with the sum of the reserved quantity in stock.move.lines for that product.
For example, consider: